← backResearch & IntelFounding · Free forever

Market sizing & TAM estimate

Triangulate top-down + bottom-up — single-method sizing is easy to dismiss.

by @whitney · recommended tool Claude Co-Work · 0 unlocks

ALTERNATES

Parallel AI

You'll need

CLAUDE SKILLS

xlsx

EXAMPLES · WHAT THIS PROMPT PRODUCES

Sample output

**TAM/SAM/SOM — AI meeting-notes tool for US law firms**

**Headline:** TAM ≈ $1.9B | SAM ≈ $640M | SOM (Yr 3) ≈ $19M

**Top-down**
US legal services market ≈ $390B. ~450K practicing-attorney firms. Legal-tech spend ≈ 4% of revenue → ~$15.6B legal-tech TAM. Meeting/intake documentation tooling ≈ 12% of that → **$1.87B TAM.**

**Bottom-up**
1.33M US lawyers × 60% in firms that buy software = ~800K seats. At $200/seat/yr (our price) = **$160M** for pure-play notes; widen to full intake/matter-doc workflow at avg $800/firm/mo across 200K target firms = **$1.92B** — triangulates with top-down.

**SAM** = firms 5–250 attorneys, cloud-comfortable, English-speaking ≈ 33% of TAM = **$640M.**

**SOM (Year 3)** = 3% of SAM at realistic win rate = **$19.2M ARR** (~2,000 firms).

**Sensitivity (3 assumptions)**
- Price $150 vs $250/seat → SOM swings $14M–$24M.
- Cloud-adoption 25% vs 45% → SAM $485M–$865M.
- Win rate 2% vs 4% → SOM $13M–$26M.

**Sources:** ABA Profile of the Legal Profession 2025; IBISWorld Legal Services; Gartner legal-tech spend benchmarks; internal pricing model.

PRICE HISTORY0 cr · current

PROMPT TEMPLATE · v1.0.0

Estimate TAM/SAM/SOM for <PRODUCT> in <REGION>.
Approach: show top-down and bottom-up, then triangulate.
Inputs: <KNOWN_DATA>.
Deliver: the number, the model (cells + formulas), sensitivity analysis on 3 key assumptions, sources.